diff --git a/Chapter13/LMM_and_Graphs.ipynb b/Chapter13/LMM_and_Graphs.ipynb new file mode 100644 index 0000000..d22bcf3 --- /dev/null +++ b/Chapter13/LMM_and_Graphs.ipynb @@ -0,0 +1,5139 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "vWNgkL71ALf7" + }, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "# install PyG if running on Google Colab\n", + "import sys\n", + "import torch\n", + "\n", + "if 'google.colab' in sys.modules:\n", + "\n", + " def format_pytorch_version(version):\n", + " return version.split('+')[0]\n", + "\n", + " TORCH_version = torch.__version__\n", + " TORCH = format_pytorch_version(TORCH_version)\n", + "\n", + " def format_cuda_version(version):\n", + " return 'cu' + version.replace('.', '')\n", + "\n", + " CUDA_version = torch.version.cuda\n", + " CUDA = format_cuda_version(CUDA_version)\n", + "\n", + " !pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n", + " !pip install torch-geometric" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aH3Q56MGDARI" + }, + "source": [ + "Let's create a toy dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388, + "referenced_widgets": [ + "a0f9087c1517444d9d02d3ee239871c0", + "49409686002944ba913043f9e566dec6", + "9ca2c22d54a14543a0ccb418f84dfc7a", + "6ef1fa9fc050493a88aa2ba634c36cb4", + "95dde357b3704448bfdb0b3a8818413e", + "918e3e0abaaa43119b27a725d9996622", + "c45bf94c8ae1497bb5093dd459e35868", + "78a75c6a1b0444baa32a8dc0444c6e42", + "efae36a6383c472c8d8fe7f39223a13f", + "722fd967e7a640468b2c1c828c6ad983", + "9b6d7d228f25418aaf2489fedc465492", + "0b92a27e384b4ccfa1717e2898c44499", + "694e7d436acc4f5288c6dddcb1e8eb43", + "f8dd5aa75d5342b9be4b9ef0914b3330", + "577d03563d0e429da232b47df6dc2cfb", + "da4c66ea361b4ea4be05010c02866f51", + "f500df21edf1499c83e6d0036e5b771c", + "26329100e08f452bba6a2cb0199c84b7", + "bcbc986b382a4ba49bfc4d5fd428488c", + "bced6297dc0b46ea826ee40344c21b13", + "04e572cf4bfe4c4b91b08f47cdb88c77", + "1c795237b0964f4182a2f2948b747f94", + "63bc10c1908945928df1d9b492912a04", + "a87bb9d072ee43a08a97c9effb82db9c", + "000c7c5876a04721ac8ccc89d9e6163e", + "ccbc8df9abab48948c5317bcc31612e1", + "87df42e6746e4847864eda694620750f", + "a88f61637e6541f589187d5d9abcb503", + "bb0d43a5c4f34b1d9a39230b9c129fd5", + "5bf85ad0b72a4e68914487ffabef5ea6", + "f8d2202f01874c30a3ae563e6fdd6428", + "853f6e10e86f4d8aba5822790d43a34e", + "581e482879434621bb48245bc886baa2", + "b369bdad965b4b3794af271f5416d42a", + "07c929ea8b0e4f6a85bfa54b60beec27", + "e2e8b3e0ceb7405fa6c0940730f60804", + "0c381c9ceeb04677903611735c1b7fcf", + "90fc7f82fa7c4fe79386e766fe687370", + "02035c4cf9f142c6a7167d4d81befce0", + "0ef9969aa23348e39e56981b0ce9ba35", + "ba9d04c9c86e45f9a80f3a61ee220c5e", + "317a758830264800bbd6f17ee10d5e22", + "a54841dde9cd42db85b326faaf4ab0b5", + "72c8076ed09748e1a37b21f25d12dafe", + "b78234a537214afebcfdee5f32d4d9a0", + "4090d3b6cdbf45e9b54f65e55a994713", + "41bb03671ae14450aaeb13b9ee9a9ffa", + "0825bfd7305b497abaac3d5edbcfb72d", + "03c8fbe4a1ab403dae44d77ff0240d9a", + "204a396320264c7fa63b2e539f1b25d1", + "6a2f31e069e54cfb96dfbb5b80e5d15b", + "45dc666b5ac94bbf9164058d0c7cd3e2", + "07718c8acd3049c4a38778e6f8d4eca3", + "e053ba80dc79470d8b1d1c9dc67db792", + "bab624c7fda8476d974253c9fba8a827" + ] + }, + "id": "YxK0emCj_0ad", + "outputId": "f823051a-2dab-49df-b472-5373cde384a1" + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "from torch_geometric.nn import GCNConv\n", + "from torch_geometric.data import Data\n", + "from transformers import AutoTokenizer, AutoModel\n", + "\n", + "# Toy dataset setup\n", + "data = Data(\n", + " x=torch.rand(3, 10), # Random node features\n", + " edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long).t().contiguous(), # Edges (transposed for PyG)\n", + " y=torch.tensor([0, 1, 2], dtype=torch.long), # True labels (3 classes)\n", + " text=[\"Paper A abstract about machine learning\", \n", + " \"Paper B abstract about deep learning\", \n", + " \"Paper C abstract about neural networks\"], # Text data\n", + ")\n", + "\n", + "# 1. Define the Graph Neural Network (GNN)\n", + "class GNN(torch.nn.Module):\n", + " def __init__(self, input_dim, hidden_dim, num_classes):\n", + " super(GNN, self).__init__()\n", + " self.conv1 = GCNConv(input_dim, hidden_dim)\n", + " self.conv2 = GCNConv(hidden_dim, num_classes) # Output num_classes\n", + " self.dropout = torch.nn.Dropout(0.2)\n", + " \n", + " def forward(self, x, edge_index):\n", + " x = self.conv1(x, edge_index)\n", + " x = F.relu(x)\n", + " x = self.dropout(x)\n", + " x = self.conv2(x, edge_index)\n", + " return x # Return logits (not softmax)\n", + "\n", + "# 2. Define the Text Encoder (BERT-based)\n", + "class TextEncoder(torch.nn.Module):\n", + " def __init__(self, model_name=\"bert-base-uncased\", num_classes=3):\n", + " super(TextEncoder, self).__init__()\n", + " self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " self.model = AutoModel.from_pretrained(model_name)\n", + " # Project from BERT's hidden size to number of classes\n", + " self.classifier = torch.nn.Linear(self.model.config.hidden_size, num_classes)\n", + " self.dropout = torch.nn.Dropout(0.1)\n", + " \n", + " def forward(self, texts):\n", + " # Tokenize and encode text data\n", + " inputs = self.tokenizer(texts, return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n", + " \n", + " with torch.no_grad(): # Freeze BERT parameters during training\n", + " outputs = self.model(**inputs)\n", + " \n", + " # Use [CLS] token embedding\n", + " cls_embedding = outputs.last_hidden_state[:, 0, :]\n", + " cls_embedding = self.dropout(cls_embedding)\n", + " logits = self.classifier(cls_embedding)\n", + " return logits # Return logits (not softmax)\n", + "\n", + "# 3. Initialize models with consistent output dimensions\n", + "num_classes = len(torch.unique(data.y)) # Number of unique classes\n", + "input_dim = data.x.size(1) # Node feature dimension\n", + "hidden_dim = 64\n", + "\n", + "gnn = GNN(input_dim=input_dim, hidden_dim=hidden_dim, num_classes=num_classes)\n", + "text_encoder = TextEncoder(num_classes=num_classes)\n", + "\n", + "# 4. Training Loop with Bidirectional Pseudo-label Exchange\n", + "def train_prediction_alignment(data, gnn, text_encoder, num_iterations=5):\n", + " optimizer_gnn = torch.optim.Adam(gnn.parameters(), lr=0.01)\n", + " optimizer_text = torch.optim.Adam(text_encoder.parameters(), lr=0.0001)\n", + " \n", + " # Initialize with true labels for first iteration\n", + " gnn_pseudo_labels = data.y.clone()\n", + " llm_pseudo_labels = data.y.clone()\n", + " \n", + " for iteration in range(num_iterations):\n", + " # 4.1 Train GNN using LLM pseudo-labels from previous iteration\n", + " gnn.train()\n", + " optimizer_gnn.zero_grad()\n", + " gnn_logits = gnn(data.x, data.edge_index)\n", + " gnn_loss = torch.nn.CrossEntropyLoss()(gnn_logits, llm_pseudo_labels)\n", + " gnn_loss.backward()\n", + " optimizer_gnn.step()\n", + " \n", + " # Generate new GNN pseudo-labels\n", + " with torch.no_grad():\n", + " gnn_pseudo_labels = torch.argmax(gnn_logits, dim=1)\n", + " \n", + " # 4.2 Train Text Encoder using GNN pseudo-labels\n", + " text_encoder.train()\n", + " optimizer_text.zero_grad()\n", + " text_logits = text_encoder(data.text)\n", + " llm_loss = torch.nn.CrossEntropyLoss()(text_logits, gnn_pseudo_labels)\n", + " llm_loss.backward()\n", + " optimizer_text.step()\n", + " \n", + " # Generate new LLM pseudo-labels for next iteration\n", + " with torch.no_grad():\n", + " llm_pseudo_labels = torch.argmax(text_logits, dim=1)\n", + " \n", + " print(f\"Iteration {iteration+1}: GNN Loss = {gnn_loss.item():.4f}, LLM Loss = {llm_loss.item():.4f}\")\n", + " print(f\" GNN predictions: {gnn_pseudo_labels.tolist()}\")\n", + " print(f\" LLM predictions: {llm_pseudo_labels.tolist()}\")\n", + "\n", + "\n", + "print(f\"Dataset info:\")\n", + "print(f\" Number of nodes: {data.x.size(0)}\")\n", + "print(f\" Node feature dimension: {data.x.size(1)}\")\n", + "print(f\" Number of edges: {data.edge_index.size(1)}\")\n", + "print(f\" Number of classes: {num_classes}\")\n", + "print(f\" True labels: {data.y.tolist()}\")\n", + "print(\"\\nStarting training...\")\n", + "\n", + "train_prediction_alignment(data, gnn, text_encoder, num_iterations=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AwmGzCmH_669", + "outputId": "2577a628-8f1c-4efb-c732-1dfbbade06bf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1: Loss = 1.1030889749526978\n", + "Epoch 2: Loss = 0.8914819359779358\n", + "Epoch 3: Loss = 0.654697060585022\n", + "Epoch 4: Loss = 1.1298424005508423\n", + "Epoch 5: Loss = 1.1417795419692993\n", + "Epoch 6: Loss = 1.0708292722702026\n", + "Epoch 7: Loss = 0.987954318523407\n", + "Epoch 8: Loss = 1.0997623205184937\n", + "Epoch 9: Loss = 1.0972650051116943\n", + "Epoch 10: Loss = 1.0914229154586792\n" + ] + } + ], + "source": [ + "# Import libraries\n", + "import torch\n", + "import torch.nn.functional as F\n", + "from transformers import AutoTokenizer, AutoModel\n", + "from torch_geometric.nn import GraphConv\n", + "from torch_geometric.data import Data\n", + "\n", + "# 1. Define the GNN\n", + "class GNN(torch.nn.Module):\n", + " def __init__(self, input_dim, hidden_dim):\n", + " super(GNN, self).__init__()\n", + " self.conv = GraphConv(input_dim, hidden_dim)\n", + "\n", + " def forward(self, x, edge_index):\n", + " return self.conv(x, edge_index)\n", + "\n", + "# 2. Define the Text Encoder (LLM)\n", + "class TextEncoder(torch.nn.Module):\n", + " def __init__(self, model_name=\"bert-base-uncased\", output_dim=128):\n", + " super(TextEncoder, self).__init__()\n", + " self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " self.model = AutoModel.from_pretrained(model_name)\n", + " self.fc = torch.nn.Linear(self.model.config.hidden_size, output_dim)\n", + "\n", + " def forward(self, texts):\n", + " inputs = self.tokenizer(texts, return_tensors=\"pt\", padding=True, truncation=True)\n", + " outputs = self.model(**inputs)\n", + " cls_embedding = outputs.last_hidden_state[:, 0, :] # [CLS] token embedding\n", + " return self.fc(cls_embedding)\n", + "\n", + "# 3. Contrastive Learning Objective\n", + "def contrastive_loss(graph_emb, text_emb, tau=0.1):\n", + " sim = F.cosine_similarity(graph_emb.unsqueeze(1), text_emb.unsqueeze(0), dim=2)\n", + " labels = torch.arange(sim.size(0)).to(sim.device)\n", + " loss = F.cross_entropy(sim / tau, labels)\n", + " return loss\n", + "\n", + "# 4. Training Loop for Latent Space Alignment\n", + "def train_latent_alignment(data, gnn, text_encoder, epochs=10):\n", + " optimizer = torch.optim.Adam(list(gnn.parameters()) + list(text_encoder.parameters()), lr=0.001)\n", + " for epoch in range(epochs):\n", + " optimizer.zero_grad()\n", + "\n", + " # Encode graph and text\n", + " graph_emb = gnn(data.x, data.edge_index) # Graph embeddings\n", + " text_emb = text_encoder(data.text) # Text embeddings\n", + "\n", + " # Compute contrastive loss\n", + " loss = contrastive_loss(graph_emb, text_emb)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " print(f\"Epoch {epoch+1}: Loss = {loss.item()}\")\n", + "\n", + "# 5. Example Data\n", + "# Toy data with 3 products and their relationships\n", + "data = Data(\n", + " x=torch.rand(3, 10), # Node features\n", + " edge_index=torch.tensor([[0, 1], [1, 2]], dtype=torch.long), # Edges\n", + " text=[\"Product A description\", \"Product B description\", \"Product C description\"], # Text data\n", + ")\n", + "\n", + "# Initialize models and train\n", + "gnn = GNN(input_dim=10, hidden_dim=128)\n", + "text_encoder = TextEncoder()\n", + "train_latent_alignment(data, gnn, text_encoder)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mqcZZn6qCCmj" + }, + "source": [ + "# GraphRAG" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xM73g-bDiPPG" + }, + "source": [ + "If using Colab you can simply run the following cells.\n", + "\n", + "Otherwise, if you want to use the local backend, please:\n", + "- download neo4j desktop on [docker](https://neo4j.com/docs/graph-data-science/current/installation/installation-docker/)*\n", + "- download [lm-studio](https://lmstudio.ai/) and download the minicpm-llama3-v-2_5 and nomic-embed-text model\n", + "\n", + "*run docker as:\n", + "\n", + "\n", + "```\n", + "docker run --rm --env NEO4J_AUTH=neo4j/defaultpass -p 7474:7474 -p 7687:7687 -v $PWD/data:/data -v $PWD/plugins:/plugins --name neo4j-apoc -e NEO4J_apoc_export_file_enabled=true -e NEO4J_apoc_import_file_enabled=true -e NEO4J_apoc_import_file_use__neo4j__config=true -e NEO4J_PLUGINS=\\[\\\"apoc-extended\\\"\\] neo4j\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "JKEORJfWwI7w" + }, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "if 'google.colab' in sys.modules:\n", + " LLM_BACKEND = \"ollama\" # choose [\"ollama\" | \"lm-studio\"]\n", + "else:\n", + " LLM_BACKEND = \"lm-studio\"\n", + "\n", + "assert LLM_BACKEND in [\"ollama\", \"lm-studio\"]\n", + "\n", + "if LLM_BACKEND == \"ollama\":\n", + " base_url = \"http://localhost:11434/v1\"\n", + " api_key = \"ollama\"\n", + " llm_model = \"phi4\"\n", + "else:\n", + " base_url = \"http://localhost:1234/v1\"\n", + " api_key = \"lm-studio\"\n", + " llm_model = \"minicpm-llama3-v-2_5\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FIEW2lBMqp2V" + }, + "source": [ + "If Colab you need to download ollama and start the server" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ha3JtCsVj5ij", + "outputId": "bf15bcb3-cd93-45f4-912f-9dfa5a35d012" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nohup: redirecting stderr to stdout\n", + "HTTP/1.1 200 OK\n", + "\u001b[1mContent-Type\u001b[0m: text/plain; charset=utf-8\n", + "\u001b[1mDate\u001b[0m: Sun, 02 Feb 2025 21:36:50 GMT\n", + "\u001b[1mContent-Length\u001b[0m: 17\n", + "\n", + "Ollama is running\n", + "real\t0m0.009s\n", + "user\t0m0.005s\n", + "sys\t0m0.004s\n" + ] + } + ], + "source": [ + "if 'google.colab' in sys.modules:\n", + " !curl https://ollama.ai/install.sh | sh\n", + " !nohup ollama serve > ollama.log &\n", + " !sleep 5\n", + " !time curl -i localhost:11434" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JPGUVFm_1eoB", + "outputId": "391fd502-f552-45df-d655-6b1af61a7830" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25lpulling manifest ⠋ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠸ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠦ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 0% ▕▏ 0 B/4.4 GB 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5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 72% ▕▏ 3.2 GB/4.4 GB 218 MB/s 5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 72% ▕▏ 3.2 GB/4.4 GB 218 MB/s 5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 73% ▕▏ 3.2 GB/4.4 GB 218 MB/s 5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 73% ▕▏ 3.2 GB/4.4 GB 218 MB/s 5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 74% ▕▏ 3.3 GB/4.4 GB 218 MB/s 5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 74% ▕▏ 3.3 GB/4.4 GB 218 MB/s 5s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 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3s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 83% ▕▏ 3.7 GB/4.4 GB 231 MB/s 3s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 83% ▕▏ 3.7 GB/4.4 GB 231 MB/s 3s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 84% ▕▏ 3.7 GB/4.4 GB 231 MB/s 3s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 84% ▕▏ 3.7 GB/4.4 GB 231 MB/s 3s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 84% ▕▏ 3.7 GB/4.4 GB 231 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 85% ▕▏ 3.8 GB/4.4 GB 231 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 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"pulling f8a805e9e620... 15% ▕▏ 154 MB/1.0 GB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 16% ▕▏ 166 MB/1.0 GB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 19% ▕▏ 203 MB/1.0 GB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 23% ▕▏ 238 MB/1.0 GB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 25% ▕▏ 261 MB/1.0 GB 261 MB/s 2s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 53% ▕▏ 554 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 57% ▕▏ 595 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 59% ▕▏ 616 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 62% ▕▏ 647 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 65% ▕▏ 680 MB/1.0 GB 277 MB/s 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 66% ▕▏ 693 MB/1.0 GB 277 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 695 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 695 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 695 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 695 MB/1.0 GB 231 MB/s 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 697 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 697 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 697 MB/1.0 GB 231 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 700 MB/1.0 GB 175 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 67% ▕▏ 701 MB/1.0 GB 175 MB/s 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 78% ▕▏ 812 MB/1.0 GB 175 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 80% ▕▏ 834 MB/1.0 GB 175 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 81% ▕▏ 845 MB/1.0 GB 175 MB/s 1s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 85% ▕▏ 888 MB/1.0 GB 175 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 88% ▕▏ 919 MB/1.0 GB 181 MB/s 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"pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 99% ▕▏ 1.0 GB/1.0 GB 181 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB/1.0 GB 181 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB/1.0 GB 181 MB/s 0s\u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB 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+ "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", + "pulling 60ed67c565f8... 100% ▕▏ 506 B \n", + "pulling 8603ca877636... 100% ▕▏ 5.7 KB \n", + "pulling f02dd72bb242... 100% ▕▏ 59 B \n", + "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 262843d4806a... 100% ▕▏ 4.4 GB \n", + "pulling f8a805e9e620... 100% ▕▏ 1.0 GB \n", + "pulling 60ed67c565f8... 100% ▕▏ 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f02dd72bb242... 100% ▕▏ 59 B \n", + "pulling 175e3bb367ab... 100% ▕▏ 566 B \n", + "verifying sha256 digest \n", + "writing manifest \n", + "success \u001b[?25h\n", + "\u001b[?25lpulling manifest ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠙ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠹ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠼ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest ⠴ \u001b[?25h\u001b[?25l\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 0% ▕▏ 0 B/274 MB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 1% ▕▏ 2.8 MB/274 MB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 3% ▕▏ 9.5 MB/274 MB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 8% ▕▏ 22 MB/274 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+ "pulling 970aa74c0a90... 100% ▕▏ 274 MB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 0% ▕▏ 0 B/ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 0% ▕▏ 0 B/ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \n", + "pulling ce4a164fc046... 0% ▕▏ 0 B/ 17 B 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MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \n", + "pulling ce4a164fc046... 100% ▕▏ 17 B \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \n", + "pulling ce4a164fc046... 100% ▕▏ 17 B \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \n", + "pulling ce4a164fc046... 100% ▕▏ 17 B \u001b[?25h\u001b[?25l\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1G\u001b[A\u001b[2K\u001b[1Gpulling manifest \n", + "pulling 970aa74c0a90... 100% ▕▏ 274 MB \n", + "pulling c71d239df917... 100% ▕▏ 11 KB \n", + "pulling ce4a164fc046... 100% ▕▏ 17 B 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embedder\n", + "# WARNING: models are > 5GB\n", + "if LLM_BACKEND == \"ollama\":\n", + " !ollama pull minicpm-v # Alternatively, try a more specialized model such as neo4j/text-to-cypher-Gemma-3-4B-Instruct-2025.04.0\n", + " !ollama pull nomic-embed-text\n", + " !ollama list" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bK17PDF7KxCv" + }, + "source": [ + "# Neo4j" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "r53ouOX_M3of", + "outputId": "6ab70449-f250-4216-b0f5-66e6147cd1ae" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "update-alternatives: error: alternative /usr/lib/jvm/java-17-openjdk-amd64/jre/bin/java for java not registered; not setting\n", + "openjdk version \"17.0.13\" 2024-10-15\n", + "OpenJDK Runtime Environment (build 17.0.13+11-Ubuntu-2ubuntu122.04)\n", + "OpenJDK 64-Bit Server VM (build 17.0.13+11-Ubuntu-2ubuntu122.04, mixed mode, sharing)\n", + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 151M 100 151M 0 0 217M 0 --:--:-- --:--:-- --:--:-- 217M\n", + "Directories in use:\n", + "home: /content/nj\n", + "config: /content/nj/conf\n", + "logs: /content/nj/logs\n", + "plugins: /content/nj/plugins\n", + "import: /content/nj/import\n", + "data: /content/nj/data\n", + "certificates: /content/nj/certificates\n", + "licenses: /content/nj/licenses\n", + "run: /content/nj/run\n", + "Starting Neo4j.\n", + "Started neo4j (pid:8890). It is available at http://localhost:7474\n", + "There may be a short delay until the server is ready.\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "\n", + "if 'google.colab' in sys.modules:\n", + " !pip install -q \"neo4j-graphrag[ollama]\"\n", + " !pip install -q langchain_community langchain_experimental langchain_neo4j langchain_openai\n", + "\n", + " # Install the JDK\n", + " !apt-get install openjdk-17-jdk-headless -qq > /dev/null\n", + " os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-17-openjdk-amd64\"\n", + " !update-alternatives --set java /usr/lib/jvm/java-17-openjdk-amd64/jre/bin/java\n", + " !java -version\n", + "\n", + " # Download Neo4j\n", + " !curl -O https://dist.neo4j.org/neo4j-community-5.26.1-unix.tar.gz\n", + " # Decompress and rename the directory\n", + " !tar -xf neo4j-community-5.26.1-unix.tar.gz\n", + " !mv neo4j-community-5.26.1 nj\n", + "\n", + " # (Optional) Disable authentication (if desired)\n", + " !sed -i '/#dbms.security.auth_enabled/s/^#//g' nj/conf/neo4j.conf\n", + "\n", + " # Copy the APOC jar from the labs directory to plugins\n", + " !cp nj/labs/apoc-*.jar nj/plugins/\n", + "\n", + " # Update configuration to allow APOC procedures\n", + " !echo \"dbms.security.procedures.unrestricted=apoc.*\" >> nj/conf/neo4j.conf\n", + " !echo \"dbms.security.procedures.allowlist=apoc.*\" >> nj/conf/neo4j.conf\n", + "\n", + " # Start Neo4j\n", + " !nj/bin/neo4j start" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uII2aMpvmT74" + }, + "outputs": [], + "source": [ + "# colab may kill the ollama process, start again\n", + "if 'google.colab' in sys.modules:\n", + " !nohup ollama serve > ollama.log &\n", + " !sleep 5\n", + " !time curl -i localhost:11434" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OUWxS0p0-d-e", + "outputId": "527d26fc-0dfc-479b-f1c8-330ec8b7d52b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nodes:[Node(id='Marie Curie', type='Person', properties={}), Node(id='1867', type='Year', properties={}), Node(id='Polish', type='Nationality', properties={}), Node(id='French', type='Nationality', properties={}), Node(id='Physicist', type='Profession', properties={}), Node(id='Chemist', type='Profession', properties={}), Node(id='Radioactivity', type='Research field', properties={}), Node(id='Nobel Prize', type='Award', properties={}), Node(id='Pierre Curie', type='Person', properties={}), Node(id='University Of Paris', type='Institution', properties={})]\n", + "Relationships:[Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='1867', type='Year', properties={}), type='BORN_IN_YEAR', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Polish', type='Nationality', properties={}), type='NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='French', type='Nationality', properties={}), type='NATURALISED_NATIONALITY', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Physicist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Chemist', type='Profession', properties={}), type='PROFESSION', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Radioactivity', type='Research field', properties={}), type='RESEARCHED_IN_FIELD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='AWARDED', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Marie Curie', type='Person', properties={}), type='SPOUSE', properties={}), Relationship(source=Node(id='Pierre Curie', type='Person', properties={}), target=Node(id='Nobel Prize', type='Award', properties={}), type='CO-WINNER_OF_AWARD', properties={}), Relationship(source=Node(id='Marie Curie', type='Person', properties={}), target=Node(id='University Of Paris', type='Institution', properties={}), type='PROFESSOR_AT_INSTITUTION', properties={})]\n", + "\n", + "Testing queries...\n", + "\n", + "Question: Who married a Nobel Prize?\n", + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mcypher\n", + "MATCH (person1:Person)-[:SPOUSE]->(person2:Person), \n", + " (person2)-[:AWARDED]->(:Award)\n", + "RETURN DISTINCT person1.id AS marriedToNobelPrizeWinner\n", + "\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'marriedToNobelPrizeWinner': 'Pierre Curie'}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "Response: Marie Curie married Pierre Curie, who was a Nobel Prize winner.\n" + ] + } + ], + "source": [ + "from neo4j import GraphDatabase\n", + "from langchain_neo4j import Neo4jGraph\n", + "\n", + "# ---- Step 1: Setup Neo4j Connection ----\n", + "NEO4J_URI = \"bolt://localhost:7687\"\n", + "NEO4J_USER = \"neo4j\"\n", + "NEO4J_PASSWORD = \"your_password\"\n", + "\n", + "driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))\n", + "graph = Neo4jGraph(url=NEO4J_URI, username=NEO4J_USER, password=NEO4J_PASSWORD)\n", + "\n", + "# ---- Step 2: Create knowledge graph from text ----\n", + "import os\n", + "from langchain_experimental.graph_transformers.llm import LLMGraphTransformer\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "llm = ChatOpenAI(temperature=0,\n", + " model_name=llm_model,\n", + " base_url=base_url,\n", + " api_key=api_key)\n", + "\n", + "llm_transformer = LLMGraphTransformer(llm=llm)\n", + "\n", + "from langchain_core.documents import Document\n", + "\n", + "text = \"\"\"\n", + "Marie Curie, born in 1867, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity.\n", + "She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields.\n", + "Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes.\n", + "She was, in 1906, the first woman to become a professor at the University of Paris.\n", + "\"\"\"\n", + "documents = [Document(page_content=text)]\n", + "graph_documents = llm_transformer.convert_to_graph_documents(documents)\n", + "print(f\"Nodes:{graph_documents[0].nodes}\")\n", + "print(f\"Relationships:{graph_documents[0].relationships}\")\n", + "\n", + "# Add graph to neo4j\n", + "graph.add_graph_documents(graph_documents)\n", + "\n", + "# ---- Step 3: Perform GraphRAG ----\n", + "\n", + "def escape(s):\n", + " return s.replace(\"{\",\"\").replace(\"}\",\"\")\n", + "\n", + "CYPHER_GENERATION_TEMPLATE = f\"\"\"You are a Neo4j expert. Generate a Cypher query to answer the given question.\n", + "\n", + "Database Schema: {escape(graph.schema)}\n", + "\n", + "Rules:\n", + "1. Always use explicit `MATCH` for relationships.\n", + "2. Never use `WHERE` for relationship matching.\n", + "3. Use `RETURN DISTINCT` when appropriate.\n", + "\n", + "Example Queries:\n", + "1. Question: \"Who won the Nobel Prize?\"\n", + " Cypher: MATCH (p:Person)-[:WON_NOBEL_PRIZE]->(:Awarded) RETURN p.id AS winner\n", + "\n", + "Question: {{query}}\n", + "Return only the Cypher query without any explanation or additional text.\n", + "Cypher:\"\"\"\n", + "\n", + "from langchain_neo4j import GraphCypherQAChain\n", + "from langchain_core.prompts import PromptTemplate\n", + "\n", + "chain = GraphCypherQAChain.from_llm(\n", + " llm=llm,\n", + " graph=graph,\n", + " verbose=True,\n", + " cypher_prompt=PromptTemplate(\n", + " input_variables=[\"query\"],\n", + " template=CYPHER_GENERATION_TEMPLATE\n", + " ),\n", + " allow_dangerous_requests=True\n", + ")\n", + "\n", + "# ---- Step 5: Test Queries ----\n", + "print(\"\\nTesting queries...\")\n", + "\n", + "question = \"Who married a Nobel Prize?\"\n", + "\n", + "print(f\"\\nQuestion: {question}\")\n", + "response = chain.invoke(question)\n", + "print(\"Response:\", response['result'])\n", + "\n", + "# Close the driver\n", + 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